Influence of Executive Function Training on BMI, Food Choice, and Cognition in Children with Obesity: Results from the TOuCH Study

Background: Children with obesity have a higher risk of future health and psychological problems. Executive functions (EFs) play a key role in successful dietetic and exercise planning; therefore, new treatments aimed at improving EFs may optimize outcomes. Objectives: This study evaluates the impact of EF training on body mass index (BMI), food choice, and cognition in children with obesity. We also examine their real-life executive functioning, emotional state, and quality of life. Methods: Randomized controlled double-blind trial. Forty-six children with obesity were randomly allocated into an executive functions training or a control task training group and attended 30–45 min of daily training (5/week over 6 weeks), with both groups receiving counseling on diet and wearing an activity/sleep tracker. Participants were evaluated at baseline and after treatment. Results: BMI decreased over time in the whole sample, although there were no differences between groups at post-training in BMI, food choice, and cognition. Both groups showed significant improvements in attention, speed, cognitive flexibility, and inhibitory control. Additionally, there were some benefits in real-life executive functioning and self-esteem. Over the 6 weeks, participants showed worse food choices in both groups. Conclusions: EFs training showed a lack of significant effects. The executive function enhancement alone did not explain these changes, as there were no significant differences between the experimental groups. It might be that the control task training could also produce some benefits, and multi-component interventions might be useful for weight loss.


Introduction
Worldwide obesity has nearly tripled over the past three decades. Prevalence in youth has risen dramatically from 4% in 1975 to more than 18% in 2016 and 39 million children under the age of 5 were overweight or obese in 2020. Children with obesity have a higher risk of future obesity, are more likely to develop non-communicable diseases such as type-2 diabetes and cardiovascular diseases, have disability in adulthood, and premature death [1]. It is considered one of the most serious public health challenges of the 21st century. Furthermore, childhood obesity is also related to psychosocial and psychological problems (i.e., anxiety and depression, low self-esteem, bullying and stigma, and poorer quality of life) [2][3][4][5].

Study Design
The study was designed as a randomized controlled double-blind trial (NCT03615274). Patients were randomly allocated by the project coordinator (C.S.C.) according to a sequential number into the 'executive functions training' or 'control task training' groups after written informed consent was given by parents and verbal consent by children when they came in person to the first assessment session. Randomization was stratified by sex, age, and manual dominance. Assessments were performed by an independent psychologist (S.L.R.) and by a pediatrician, both blind to allocation. Patients and families were also blind to the allocation. The study was approved by the University of Barcelona Institutional Review Board (IRB00003099 protocol 122/V/2016), and by the Consorci Sanitari de Terrassa (02-17-503-039) and Hospital Sant Joan de Déu (PIC-02-19) Review Boards. More details of the study design can be found in Sanchez-Castañeda et al. [30].

Participants
Forty-six children with obesity were recruited from two hospitals located in the area of Barcelona: Consorci Sanitari de Terrassa and Hospital Sant Joan de Déu. Inclusion criteria were having obesity (BMI standard deviation >2 for age and sex) [31]; an age range between 9 and 12 years, including a homogeneous sample of preadolescents (to avoid the confounding factors induced by puberty), and having an intelligence quotient (IQ) within the normal range (80-120). Exclusion criteria were having a neurological, psychiatric, or developmental disorder, secondary obesity, and/or being under treatment with psychotropic drugs.
A total of 36 children completed the training (>75% of sessions completed) and posttraining assessment and were therefore included in our per-protocol analysis. Figure 1 shows the flow diagram of the study recruitment with the sample according to CONSORT reporting guidelines [32].

Sample Size
Previous literature on the field has shown significant results with similar sample sizes. A detailed review of appropriate sample sizes to evaluate changes in BMI in children with obesity can be found in our previous publication [30].

Sample Size
Previous literature on the field has shown significant results with similar sample sizes. A detailed review of appropriate sample sizes to evaluate changes in BMI in children with obesity can be found in our previous publication [30].

Intervention 2.3.1. Cognitive Training
Participants underwent 6 weeks of home-based iPad executive function training (5 sessions/week, 30-45 min/session). Motivation for treatment was assessed before they started by a Likert-type rating scale from 1 (not at all) to 7 (very much).
Participants in the experimental group underwent executive function training, which consisted of working memory training by the Cogmed software and executive function training (mainly inhibitory control, decision-making, and cognitive flexibility) by Cognifit software, both with adaptive difficulty. Cogmed is one of the five commercial cognitivetraining programs whose effectiveness has been assessed by several publications [21,33]. Cognifit has no independent studies to date including the pediatric population, although there are some on adults [34,35]. For more details of the evidence of the efficacy of these programs, see Sanchez-Castañeda et al. [30].
Participants of the active control group underwent the control task training, which consisted of the same exercises in Cognifit software but minimizing the executive component and without increasing difficulty (5 sessions/week, 30-45 min/session). We did not use control task training for Cogmed, since the platform no longer provides non-adaptive training.
The research team was in contact with parents through mobile phones once or twice a week, to check that there were no problems with training compliance and that they sent the required and reliable information, or to provide assistance if they experienced technical issues or any other reason that made the daily training difficult (i.e., illness).

Psychoeducation and Food Register
Participants received dietary and healthy lifestyle counseling using animated daily presentations displayed on Prezi (www.prezi.com, accessed on 30 January 2018), which included a variety of content such as healthy food recommendations, healthy-fun recipes (i.e., visually attractive: representing animals, cartoons, etc.), enhancing physical activity, and strategies to manage emotion and behavior.
In addition, participants were required to provide daily pictures of their food intake, sending photos of the meals through the iPad. We considered valid weekly registers the ones with 4 or more days with complete intake pictures (≥4 meals), from which one should be a weekend day.

Activity and Sleep Pattern Feedback
Participants were provided with a Fitbit Flex 2 (https://www.fitbit.com/flex2) to monitor physical activity and sleep patterns during the weeks of training. They could check their tracking on the iPads and Fitbit sent them badges and positive-feedback messages when they reached a milestone.

Outcomes
Outcomes were collected at baseline (T0), at post-training (T1; 6 weeks after baseline), and during the training period. The assessment consisted of:

Pediatric Assessment
Personal and familial medical history, anthropometric measures (height, weight, waist circumference), and food-choice habits (original Kidmed questionnaire) [36] were assessed at T0 and T1 and considered as far-transfer primary measures.

Neuropsychological Assessment
Verbal and visual IQ was estimated by the vocabulary and the matrix reasoning subtests of the Wechsler Intelligence Scale for Children-Fifth edition (WISC-V, Spanish version) [37] to control for potential differences in IQ between groups at baseline.
Socioeconomic data from family and school information were also obtained to characterize the sample. Socioeconomic data were computed by considering familiar annual incomes and parents' educational level and profession.
According to our main purpose, the following cognitive domains were evaluated at T0 and T1: Attention and speed: measured by the forward digit span of WISC-V, the forward spatial span of Wechsler non-verbal (WNV) [38], the part I of Children's Color Trail Test (CCTT) [39], the reading and counting subtests of Five Digits Test (FDT)002C [40], and the Conners' Continuous Performance Test 3 (CPT3) [41].
Executive functions: We evaluated the three core subdomains proposed by Diamond [28]: (a) cognitive flexibility was evaluated by the shifting subtest of FDT and part II of CCTT; (b) working memory was assessed by the backward digit span of WISC-V, the backward spatial span of WNV, and custom N-back task [42]; and (c) inhibitory control was assessed by the choosing subtest of FDT and a go-no go task [43]. Planning was also measured, using the Tower of London test [44].
All cognitive scores were z-scaled and then added into a global composite considered as a near-transfer primary measure. Attention and executive function subdomains were further analyzed by a post hoc analysis with Bonferroni correction for multiple comparisons. For more details on the neuropsychological assessment, see Sanchez-Castañeda et al. [30].

Rating Scales (Quality of Life, Emotional, and Behavioral Measures)
We assessed executive function behaviors by the Behavior Rating Inventory of Executive Function 2 (BRIEF-2) [45]. Quality of life was evaluated by the Pediatric Quality of Life Inventory (PedsQL v4.0) [46]. Children's self-esteem and perceived social support were assessed by the Self-Perception and Social Support Profile for Children (SPPC) test [47]. Internalizing and externalizing symptoms were assessed by the Child Behavior Checklist (CBCL) [48]. All rating scale measures were further analyzed by a post hoc analysis and considered far-transfer secondary outcomes. For more details, see Sanchez-Castañeda et al. [30].

Food Choice
Pictures of the daily intake were collected in tables to assess the type of food per day and week. Weekly data from intake pictures were rated by two assessors (S.L.R. and S.M.G.) using an in-house modified version of the Kidmed questionnaire [36]. The original questionnaire does not allow the quantification of meat, eggs, potato, processed food, and extra intake. Therefore, we extended the questionnaire, according to our nutritionist counseling (S.M.), and included 7 items that collected and quantified these categories. In the new custom scale, called from now on Kidmed-modified, the total values range from 0 to 15, with higher scores meaning better food choices. Inter-rater assessment consistency was evaluated and in case of discrepancy solved by a third rater or a review and discussion of the data. A change in food choice was also considered a primary measure.

Physical Activity and Sleep Pattern
Daily steps and minutes of sleep were also outcomes of interest. For research purposes, we considered a week as valid in the register when it had ≥4 complete days, defined as those with ≥10 h of any type of physical activity [49]. Fitbit non-wear time was defined as a period of ≥60 min with no steps registered [50].

Statistical Analysis
We performed a descriptive analysis for demographic variables comparing the groups at baseline (chi-square test or Fisher's exact, Student's t test or Mann-Whitney's U test, see Table 1) to control for possible confounding variables (age, sex, IQ, handedness, economic income). Other relevant variables were also controlled (physical activity, initial motivation for treatment, hospital of provenance). For the evaluation of the effectiveness of treatment on anthropometrics, cognition, and rating scales, we performed mixed ANOVA with time as a 2-level within-subjects factor (T0, T1) and the group as an inter-subject condition. We used an intention-to-treat (ITT) approach and list-wise deletion was applied, analyzing the available data without imputing the unknown values. Multiple comparisons were tested by Bonferroni correction.
For longitudinal data (food choice, physical activity, and sleep patterns over 6 weeks), linear mixed models (LMM) with a random intercept and slope were used. Group and group per time interaction were set as fixed effects, and the subject's ID was considered as a random effect in all these models.
Finally, following the Consort guidelines, [32] we ran an additional analysis on treatment adherence by comparing baseline characteristics between participants who reached a satisfactory training level (≥75%, completers) and who did not (<75%, non-completers).
All statistical tests were carried out using a two-sided test with the significance level set at 5% with the software Statistical Package for the Social Sciences (SPSS, version 24.0).

Results
Descriptive data and group comparison at baseline are shown in Table 1. No significant differences in baseline characteristics were found between groups, which were equally balanced for age, sex, handedness, IQ, economic status, hospital, BMI, physical activity, and motivation for treatment. Groups did not differ in any anthropometric and diet measures, neuropsychological variables, or rating scales (except for internalizing symptoms on CBCL, p = 0.03) (see Supplementary Table S1). Intention-to-treat analysis showed similar results but without any differences for internalizing symptoms on CBCL (see Supplementary Tables S2 and S3).

Cognition, Anthropometric Measures, and Food Choice
Changes from T0 to T1 in cognition, BMI, waist circumference (WC), and Kidmed questionnaire did not significantly differ between the two conditions, as shown in Table 2a. However, some within-group changes were found. There was a significant decrease over time in BMI in both groups (F (1,34) = 8.81; p = 0.005; η p 2 = 0.206) and also a reduction in waist circumference (F (1,33) = 8.22; p = 0.007; η p 2 = 0.199). ITT analysis showed similar results (see Supplementary Table S4a).

Evolution of Food Choice across Six-Week Training
Descriptive data for food choice are shown in Supplementary Tables S7 and S8. Results from mixed effects models (Table 2b) suggested that there were no significant intervention group effects on food choice (β = 0.387 (CI −1.079 to 1.853), SE = 0.715, p = 0.593). However, there was a significant effect over time showing a tendency towards worse food choices in both groups (β = −0.926 (CI −1.806 to −0.045), SE = 0.427, p = 0.040). ITT analyses revealed similar results, although the effect of time on food choices was not significant (see Table S4b).

Cognitive Subdomains
As shown in Table 3, significant improvements over time were found in attention and speed, working memory, cognitive flexibility, inhibitory control, and planning in both groups. Concrete benefits in attention and speed were detected in WNV spatial span forward (F (1,34)  ITT analyses (see Table S5) revealed similar results with some exceptions: there was no significant group-by-time interaction in WNV spatial span backward and N-back-2. The improvement in FDT reading over time was also non-significant. After Bonferroni correction, improvements over time remained significant in attention and speed (FDT counting time, p = 0.000), cognitive flexibility (CCTT-II, p = 0.000; FDT shifting time, p = 0.000), inhibitory control (FDT choosing time, p = 0.000; go-no go commissions, p = 0.000), and planning (ToL time, p = 0.001).

Rating Scales
As displayed in Table 4a, changes from T0 to T1 did not significantly differ between the two conditions in rating scales, as there were no significant effects of time by group interaction on any measures. However, some within-group changes were found in both groups. There was a significant improvement over time in BRIEF-2 subscales: cognitive (F (1,34)     ITT analysis showed similar results but the improvement in time in BRIEF-2 behavioral subscale and CBCL externalizing symptoms did not reach significance (see Table S6a). Moreover, none of the results remained significant after the Bonferroni correction.

Evolution of Healthy Habits (Physical Activity and Sleep Patterns)
Descriptive data for physical activity and sleep patterns over the 6 weeks are shown in Supplementary Table S7. Results from mixed effects models (Table 4b) Table S6b).

Additional Analysis of Treatment Adherence
As shown in Table 5, of the 46 participants initially randomized (experimental group, n = 27; control group, n = 19), 36 completed satisfactory training (≥75% of sessions completed), which means a global adherence of 78.26%. Considering the group allocation, the experimental group showed an adherence of 70.3% and the control group of 89.17%. These differences did not reach statistical significance (p = 0.160). Independently of the allocation group, baseline characteristics of completer-and non-completer participants differed in several aspects. Completer-participants showed higher physical activity levels (p = 0.004), higher estimated visual IQ (p = 0.006), and increased motivation for treatment (p = 0.048). Furthermore, a trend for significance was found in the child-reported quality of life (PedsQl, p = 0.076) and perceived social support (SPPC, p = 0.083), with higher scores in completer participants.

Discussion
This study is one of the first to evaluate the impact of executive function training in children with obesity, the main objective being to present its effects on BMI, food choice, and cognition. The results revealed no significant differences between the experimental group and the active control group on these measures, but some within-group changes were found in BMI and waist circumference, which decreased over time in both. Similarly, post hoc analyses of specific cognitive subdomains revealed significant changes over time in attention and speed, working memory, cognitive flexibility, inhibitory control, and planning. These improvements did not significantly differ between groups, except for visual working memory, in which the experimental group performed better. After a restrictive correction, the effects remained still significant in several tests of attention and speed, cognitive flexibility, and inhibitory control. Regarding food choice, while results obtained from the Kidmed questionnaire indicated no changes post-training, the analysis of the daily food register showed a tendency for worse food choices for both groups over time.
All these findings imply that the executive function training with increasing difficulty did not produce stronger BMI decreases or better food decisions than the executive function training with the same difficulty, contrary to what we hypothesized. A plausible explanation is that we overestimated the difference in the executive function requirements between groups and they conducted a similar intervention, with other shared components that could also account for the observed effect. Therefore, we cannot conclude that the executive function training was directly linked with the observed effects, because of this limitation of the study [51]. Furthermore, another plausible explanation, according to previous research examining this issue, is that the most consistent effects on weight loss are seen after food-specific inhibition training, in contrast to other types of cognitive training [52]. In this regard, although we include inhibitory control in our training, it was designed with non-food-specific stimuli. Similarly to ours, a previous study with similar training characteristics (working memory and inhibitory control training with non-specific stimuli over 6 weeks) found no changes in BMI after training [22]. Regarding food decisions, some studies have revealed the potential of cognitive training to modify eating behavior (i.e., food choices in the laboratory) [52] but, typically, these studies measure food choice in a laboratory after relatively short interventions. In this sense and to date, there are no studies of cognitive training with a measure of food choices such as ours.
Regardless of the group, there was a reduction in BMI and waist circumference after training and a worsening in food choice over the training period. This was surprising, as it was expected that a decrease in weight measures should be associated with better food choices, especially if physical activity levels remained stable. We have some plausible explanations that are not mutually exclusive. First, participants could have started with high levels of intrinsic motivation because of the novelty of intervention, [53] showing better food choices at the beginning and then returning to their previous diet because of habituation to the stimuli. Second, initially, being monitored by an external health professional could have produced some discomfort in consuming certain types of food or quantities; therefore, they could have modified their consumption in a more or less conscious way. Third, pre-intervention physical activity levels may have been lower, and then they increased because of wearing an activity tracker and being monitored. This may explain why participants reduced their BMI and waist circumference despite the worsening of food choices.
Regarding the secondary aim of the study, which was to evaluate the potential effect of cognitive training on children's real-life executive functioning, emotional state, and quality of life, some within-group improvements were found in executive functioning, children-reported quality of life, perceived self-esteem, social support, internalizing, and externalizing problems in both groups. Of note, some of these improvements (the cognitive subdomain of executive functioning and perceived self-esteem) remained significant after a restrictive statistical correction. Again, the executive function enhancement alone did not explain these changes, but the whole intervention had a positive effect on several real-life measures. These results are aligned with sundry reviews conducted with cognitive training studies. Simons et al. [21] described better performance in trained tasks, with fewer effects on closely related tasks (near-transfer) and even less on distantly related tasks (far-transfer). Other authors concluded that far-transfer effects on other cognitive functions and real-world measures have been limited or inconsistent throughout studies [20,21,33,54].
Overall, we found that the executive function enhancement alone did not explain these changes, as there were no significant differences between groups. It might be that the control task training could also produce some benefits, so we found that the whole intervention had a positive impact on BMI and WC, several executive functions, and some measures of emotional state and quality of life, although being from the executive training group or being from the active control cognition did not explain differences. Some previous studies with active control groups using the Cogmed and Cognifit systems also found significant changes in several cognitive domains in both groups, suggesting that the low-intensity practice could also produce some benefits [34,55,56]. Preiss et al. [56] even described significant far-transfer improvements in both groups on the Cognitive Failures Questionnaire (CFQ) [57] and a trend for significance on the Dysexecutive Questionnaire (DEX) [58], both being measures of cognitive functioning in real life and, in this sense, comparable to our results in BRIEF for children. Related to the results of weight measures, it has been well documented that childhood obesity tends to worsen over time, as BMI in these children increases more exponentially than that in their normal-weight peers, according to the growth curves of the Clinical Growth Charts of the Centers for Disease Control and Prevention [59]. Therefore, our intervention had a positive short-term impact by altering the typical course of this health condition.
As said before, there were positive effects on several measures of emotional state, quality of life, and self-esteem after the intervention. These results are probably the most interesting finding, as there are no similar studies of cognitive training assessing these factors in children with obesity. However, there is some previous literature with non-clinical school samples. De Voogd et al. [60] found no differences in anxiety, depression, self-esteem, or socio-emotional behavioral problems after visual attention training. Hitchcoch and Westwell [61] showed similar results with no differences in socio-emotional behavioral difficulties, and Roberts et al. [62] did not find significant results either after applying Cogmed. All these studies applied exclusively cognitive training; thus, it seems reasonable that cognitive training may not be entirely responsible for the observed changes in our study. Although it was not specifically tested because it was not the aim of the study, it could be possible that other intervention components (i.e., psychoeducation, monitoring food register, wearing an activity tracker) account for the effects. In fact, a very recent study in children with obesity with multiple interventions (health education, physical exercise, and diet control) observed an improvement in psychological QoL in the intervention group, with higher psychological, pubertal, and total QoL all being higher in this group [63].
Finally, regarding treatment adherence, the global rate was 78.26% (70.3% in the experimental group and 89.17% in the control group). These differences did not reach statistical significance, although there was a trend for worse compliance in the experimental group. These findings are quite different from the average attrition rate in Cogmed trials, as typically studies lost 10% of the participants in the training group and 11% of the participants in the control group [64]. However, it has been documented that in pediatric obesity management, attrition could be as high as 80%, and 30 to 40% is common [65]. Moreover, typical Cogmed trials do not include other cognitive training programs, which require even more effort and commitment for participants. In this sense, our data are in line with those of other studies in this specific population.
Additionally, in our sample, some baseline characteristics differed between completers and non-completer participants regardless of the group. The first showed higher physical activity levels, higher estimated visual IQ, and higher motivation for treatment. Furthermore, a trend was found for higher scores in self-reported quality of life and perceived social support. Previous literature reviews on factors related to attrition in pediatric obesity have revealed some important insights. For instance, Dhaliwal et al. [66] revealed that attrition was higher in older children (≥12 years old), whereas sex and baseline weight status did not predict it. Other factors explored among studies but with inconsistent results were internalizing and externalizing behavior, lower self-esteem, social problems, school problems, motivation, socioeconomic status, expectations of treatment, caregiver-rated quality of life, health status, ethnicity, home environment, success in BMI reduction during treatment, or lifestyle factors. Of note, heterogeneity is very high, and there were no clear answers on which factors contributed to dropout and what changes might improve retention [67]. Our results contribute to expanding the literature on which individual factors could be related to treatment adherence and convey the importance of examining this issue in future studies. They also pointed to the importance of perceived social support for treatment adherence.

Strengths and Limitations
This study has some strengths and some limitations. The main strengths include the three-fold intervention (executive function training, psychoeducation, and feedback on activity/sleep tracking) and its attractive delivery to children (it was implemented through an iPad, receiving feedback every day). Additionally, we evaluated transfer effect, which is an issue in most classical studies applying cognitive training that had focused primarily on assessing neuropsychological outcomes [68]. Moreover, we had a very good measure of food choice (photos of food intake), which overcomes traditional limitations of self-reported questionnaires such as bias problems [69,70]. Finally, we conducted the study with an active control group, which constitutes a more rigorous design, and makes the groups more comparable, potentially equating confounding factors and reducing motivational differences [21]. Nevertheless, active control training was selected ad hoc for our study, and the difference from experimental training was not tested before.
Regarding other limitations of the study, it was somewhat limited in statistical power due to the sample-size determination, which was based on previous literature in the field (for more details see Sanchez-Castañeda et al. [30]). In this sense, we could not introduce more explanatory factors in the statistical models to account for the observed variability over time, as this may have introduced bias in the effect size estimation. For this reason, larger randomized controlled trials with both active and passive control groups are worth considering.

Conclusions and Future Research
Obesity treatment remains a public health challenge. Our results contribute to expanding the literature on which elements may be useful to add to multicomponent interventions for weight loss. Future studies with greater statistical power are still needed to better determine which of these elements account for the most variability and which individual factors predict a better response to treatment, as well as the type of stimuli that should be used in these interventions to improve their efficacy. In addition, it is still necessary to have more ecological measures of food intake that better reflect the daily life of the patient. In this sense, studies should continue examining how to assess diet more validly and reliably. Finally, more long-term intervention studies in younger patients with obesity should be promoted for preventing chronicity in adulthood, and to prevent developing any pathology.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/brainsci13020346/s1, Table S1: Main outcomes at baseline and group comparison (PP analyses), Table S2: Baseline characteristics and group comparisons for all randomized participants (ITT), Table S3: Main outcomes at baseline and group comparison for all randomized participants (ITT), Table S4: EF training effects on primary outcomes: (a) cognition and anthropometric pre-post measures and (b) evolution of food-choice across the 6-week training period (ITT), Table S5: EF training effects on secondary outcomes: post-hoc analyses of cognitive subdomains (ITT), Table S6: EF training effects on secondary outcomes: (a) post-hoc analyses of rating scales and (b) evolution of healthy habits across the 6-week training period (ITT); Table S7: Descriptive data at each time point for diet, physical activity and sleep patterns (PP analyses); Table S8: Descriptive data at each time point for diet, physical activity and sleep patterns (ITT).  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.